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End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole.
Gradient methods for the minimisation of functionals
B.T. Polyak · 1963
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Continual Learning in Reinforcement Environments
M. Ring · 1994
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Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Ng · 2004
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Optimal ordered problem solver
J. Schmidhuber · 2004
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Off-road obstacle avoidance through end-to-end learning
Y. LeCun, U. Müller, J. Ben, E. Cosatto, and B. Flepp · 2005
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A fast learning algorithm for deep belief nets
G.E. Hinton, S. Osindero, and Y.-W. Teh · 2006
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, Li-Jia Li, Kai L., and L. Fei-Fei · 2009
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Formal theory of creativity, fun, and intrinsic motivation
J. Schmidhuber · 2010
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A committee of neural networks for traffic sign classification
D. Cireşan, U. Meier, J. Masci, and J. Schmidhuber · 2011
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Natural language processing (almost) from scratch
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G.E. Hinton · 2012
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ADADELTA: an adaptive learning rate method
M.D. Zeiler · 2012
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Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
R. Johnson and T. Zhang · 2013
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Minimizing Finite Sums with the Stochastic Average Gradient
M. Schmidt, N. Le Roux, and F. Bach · 2013
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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MatConvNet – Convolutional Neural Networks for MATLAB
A. Vedaldi and K. Lenc · 2015
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
M. Abadi et al · 2016
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Hybrid computing using a neural network with dynamic external memory
A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. Colmenarejo Gómez, E. Grefenstette, T. Ramalho, and J. Agapiou · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A. Graves, G. Wayne, and I. Danihelka · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, and G. Ostrovski · 2015
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Deep learning in neural networks: An overview
J. Schmidhuber · 2015
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P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, and K. Kavukcuoglu · 2016
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Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot · 2016
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Value iteration networks
A. Tamar, S. Levine, P. Abbeel, Y. Wu, and G. Thomas · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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